Limits...
Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data.

Lynch EP, Houghton CJ - Front Neuroinform (2015)

Bottom Line: Spiking neuron models can accurately predict the response of neurons to somatically injected currents if the model parameters are carefully tuned.Predicting the response of in-vivo neurons responding to natural stimuli presents a far more challenging modeling problem.We apply this to parameter discovery in modeling two experimental data sets with spiking neurons; in-vitro current injection responses from a regular spiking pyramidal neuron are modeled using spiking neurons and in-vivo extracellular auditory data is modeled using a two stage model consisting of a stimulus filter and spiking neuron model.

View Article: PubMed Central - PubMed

Affiliation: School of Mathematics, Trinity College Dublin Dublin, Ireland ; Department of Computer Science, University of Bristol Bristol, UK.

ABSTRACT
Spiking neuron models can accurately predict the response of neurons to somatically injected currents if the model parameters are carefully tuned. Predicting the response of in-vivo neurons responding to natural stimuli presents a far more challenging modeling problem. In this study, an algorithm is presented for parameter estimation of spiking neuron models. The algorithm is a hybrid evolutionary algorithm which uses a spike train metric as a fitness function. We apply this to parameter discovery in modeling two experimental data sets with spiking neurons; in-vitro current injection responses from a regular spiking pyramidal neuron are modeled using spiking neurons and in-vivo extracellular auditory data is modeled using a two stage model consisting of a stimulus filter and spiking neuron model.

No MeSH data available.


Comparison of model predictions and normalized reverse correlation predictions on the validation set under three metrics; (A) the coincidence factor and (B) the van Rossum distance. The average values of the coincidence factor scaled by the intrinsic reliability were Γ = 0.76 ± 0.08 for the STRF-aEIF models and Γ = 0.61 ± 0.05 for the STRF-poisson model. The STRF-aEIF model had an average better coincidence factor in 70.6% of cells. The average van Rossum distances were 1.39 ± 0.05 for the STRF-aEIF model and 1.50 ± 0.03 for the STRF-Poisson model with the STRF-aEIF performing better in 59% of cases. +refer to the individual data points; each +corresponds to an individual cell in the cohort of cells studied.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4403314&req=5

Figure 8: Comparison of model predictions and normalized reverse correlation predictions on the validation set under three metrics; (A) the coincidence factor and (B) the van Rossum distance. The average values of the coincidence factor scaled by the intrinsic reliability were Γ = 0.76 ± 0.08 for the STRF-aEIF models and Γ = 0.61 ± 0.05 for the STRF-poisson model. The STRF-aEIF model had an average better coincidence factor in 70.6% of cells. The average van Rossum distances were 1.39 ± 0.05 for the STRF-aEIF model and 1.50 ± 0.03 for the STRF-Poisson model with the STRF-aEIF performing better in 59% of cases. +refer to the individual data points; each +corresponds to an individual cell in the cohort of cells studied.

Mentions: The predictive accuracy of the cascade STRF-aEIF model vs. normalized reverse correlation STRFs realized as spiking neurons using an inhomogeneous Poisson process is illustrated in Figure 8. While there is considerable variation, in general it can be seen from Figure 8A that the STRF-aEIF model on average achieved a better coincidence factor with the validation data than a STRF-Poisson cascade spiking model; it performed better in 70.6% of the data sets with an average performance factor of Γ = 0.76 ± 0.08 vs. Γ = 0.61 ± 0.05.


Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data.

Lynch EP, Houghton CJ - Front Neuroinform (2015)

Comparison of model predictions and normalized reverse correlation predictions on the validation set under three metrics; (A) the coincidence factor and (B) the van Rossum distance. The average values of the coincidence factor scaled by the intrinsic reliability were Γ = 0.76 ± 0.08 for the STRF-aEIF models and Γ = 0.61 ± 0.05 for the STRF-poisson model. The STRF-aEIF model had an average better coincidence factor in 70.6% of cells. The average van Rossum distances were 1.39 ± 0.05 for the STRF-aEIF model and 1.50 ± 0.03 for the STRF-Poisson model with the STRF-aEIF performing better in 59% of cases. +refer to the individual data points; each +corresponds to an individual cell in the cohort of cells studied.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4403314&req=5

Figure 8: Comparison of model predictions and normalized reverse correlation predictions on the validation set under three metrics; (A) the coincidence factor and (B) the van Rossum distance. The average values of the coincidence factor scaled by the intrinsic reliability were Γ = 0.76 ± 0.08 for the STRF-aEIF models and Γ = 0.61 ± 0.05 for the STRF-poisson model. The STRF-aEIF model had an average better coincidence factor in 70.6% of cells. The average van Rossum distances were 1.39 ± 0.05 for the STRF-aEIF model and 1.50 ± 0.03 for the STRF-Poisson model with the STRF-aEIF performing better in 59% of cases. +refer to the individual data points; each +corresponds to an individual cell in the cohort of cells studied.
Mentions: The predictive accuracy of the cascade STRF-aEIF model vs. normalized reverse correlation STRFs realized as spiking neurons using an inhomogeneous Poisson process is illustrated in Figure 8. While there is considerable variation, in general it can be seen from Figure 8A that the STRF-aEIF model on average achieved a better coincidence factor with the validation data than a STRF-Poisson cascade spiking model; it performed better in 70.6% of the data sets with an average performance factor of Γ = 0.76 ± 0.08 vs. Γ = 0.61 ± 0.05.

Bottom Line: Spiking neuron models can accurately predict the response of neurons to somatically injected currents if the model parameters are carefully tuned.Predicting the response of in-vivo neurons responding to natural stimuli presents a far more challenging modeling problem.We apply this to parameter discovery in modeling two experimental data sets with spiking neurons; in-vitro current injection responses from a regular spiking pyramidal neuron are modeled using spiking neurons and in-vivo extracellular auditory data is modeled using a two stage model consisting of a stimulus filter and spiking neuron model.

View Article: PubMed Central - PubMed

Affiliation: School of Mathematics, Trinity College Dublin Dublin, Ireland ; Department of Computer Science, University of Bristol Bristol, UK.

ABSTRACT
Spiking neuron models can accurately predict the response of neurons to somatically injected currents if the model parameters are carefully tuned. Predicting the response of in-vivo neurons responding to natural stimuli presents a far more challenging modeling problem. In this study, an algorithm is presented for parameter estimation of spiking neuron models. The algorithm is a hybrid evolutionary algorithm which uses a spike train metric as a fitness function. We apply this to parameter discovery in modeling two experimental data sets with spiking neurons; in-vitro current injection responses from a regular spiking pyramidal neuron are modeled using spiking neurons and in-vivo extracellular auditory data is modeled using a two stage model consisting of a stimulus filter and spiking neuron model.

No MeSH data available.